Destination choice models can be embedded in transport and land use models to understand travel and location choice behavior and to forecast scenarios. Utility-maximizing destination choice models can account for individual behavior and make them suitable for agent-based models, while processing destination capacities is also in line with agent-based modeling. This paper addresses the possibility and impact of introducing capacity constraints, their effect on choice behavior, and the feasibility of applying an approach like this in agent-based microsimulations with individual characteristics for each agent. Here, a comprehensive workplace choice model and its application in a large-scale simulation case study for Singapore are described; one technical and one methodological achievement are highlighted. Technical achievement benefits from recent computational advances; the workplace choice model is estimated with a comprehensive utility function on a large data set with 103 destinations. Reasonable model fit and robust parameters are achieved while obviating sampling techniques; resulting parameters are efficiently applied to the entire 5.4 million Singapore population and validated with survey data. For methodological innovation, capacity limitations are introduced at workplaces to avoid oversaturation. A robust optimization method based on shadow prices is proposed to accommodate capacity limitations at all workplaces during the choice model application defined above. The proposed method efficiently assigns commuters to unused workplaces while respecting individual commuter preferences. Validation of the simulation results, by comparing travel time distributions for commuting trips reported in travel diary data, shows that the model fits well with observed dataShow more